Efficient Client Contribution Evaluation for Horizontal Federated Learning
This addresses the need for practical contribution measurement in federated learning to enable fair benefit distribution and detect malicious participants, though it is incremental as it builds on existing FL frameworks.
The paper tackles the problem of efficiently and fairly evaluating client contributions in horizontal federated learning, proposing a reinforcement learning-based method that outperforms conventional approaches in valuation authenticity and time complexity.
In federated learning (FL), fair and accurate measurement of the contribution of each federated participant is of great significance. The level of contribution not only provides a rational metric for distributing financial benefits among federated participants, but also helps to discover malicious participants that try to poison the FL framework. Previous methods for contribution measurement were based on enumeration over possible combination of federated participants. Their computation costs increase drastically with the number of participants or feature dimensions, making them inapplicable in practical situations. In this paper an efficient method is proposed to evaluate the contributions of federated participants. This paper focuses on the horizontal FL framework, where client servers calculate parameter gradients over their local data, and upload the gradients to the central server. Before aggregating the client gradients, the central server train a data value estimator of the gradients using reinforcement learning techniques. As shown by experimental results, the proposed method consistently outperforms the conventional leave-one-out method in terms of valuation authenticity as well as time complexity.